Neural Video Processing and Streaming for Real-time Traffic Monitoring

Descrizione del progetto

IA per il monitoraggio del traffico in tempo reale

La rapida urbanizzazione e l’aumento costante del numero di veicoli hanno sollevato preoccupazioni per la sicurezza stradale. Di conseguenza, sono stati installati sistemi di monitoraggio del traffico in tempo reale per assistere gli operatori nel controllo del traffico e in situazioni di emergenza. Il progetto VISIONS, finanziato dall’UE, svilupperà un sistema di monitoraggio del traffico in tempo reale con trasmissione video di alta qualità in città intelligenti basato su metodi di intelligenza artificiale nell’elaborazione e nello streaming video. Le caratteristiche dei sistemi visivi umani saranno indicate sull’allocazione della qualità video per limitare l’intervallo necessario per la trasmissione video. Verrà implementato un metodo avanzato basato su reti neurali profonde per consentire il rendering e lo streaming video a risoluzioni più basse e verrà pianificato un nuovo sistema di adattamento bitrate basato sull’apprendimento per rinforzo per garantire la qualità dell’esperienza.

Obiettivo

With the rapid development of urbanization and continuous increase of vehicles on roadways, Intelligent Transportation Systems (ITS) play a key role in revolutionizing the way people commute. To make our cities safer and smarter, real-time traffic monitoring systems are deployed to help operators with observing traffic flows and identifying emergency situations.

This project aims to achieve real-time traffic monitoring with high-quality video transmission in smart cities, leveraging the emerging Artificial Intelligence methods in video processing and video streaming. Firstly, the features of human visual systems will be referred on video quality allocation to reduce the required bandwidth for video transmission. Next, an innovative method for end-to-end video processing based on Deep Neural Networks will be developed to allow the video rendering and streaming at a lower resolution and also restore/improve the quality at the user ends. Finally, a new bitrate adaption scheme based on Reinforcement Learning will be designed to accommodate the unexpected network dynamics, guaranteeing the Quality-of-Experience to be perceived by users. The expected outcome can promote safer and more efficient travel for millions of users in Europe and billions of users all over the world. Moreover, the results of this project can be used in other multimedia applications, such as cloud virtual reality, distance education, smart transportation, and healthcare where video processing and video streaming are needed.

To broaden the fellow’s knowledge horizon, a series of research, training, and knowledge transfer activities are planned. The new knowledge and skills imparted in these activities will further promote his academic portfolio and significantly enhance his career prosperity. The project will also play a solid foundation for the long-term and wide-range collaborations and eventually lead to more extensive impact of project results, from which both EU and China will benefit.

Campo scientifico

Programma(i)

Argomento(i)

MSCA-IF-2019 - Individual Fellowships

Invito a presentare proposte

H2020-MSCA-IF-2019

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Meccanismo di finanziamento

MSCA-IF-EF-ST - Standard EF

Coordinatore

THE UNIVERSITY OF EXETER

Contribution nette de l'UE

€ 224 933,76

Indirizzo

The queen's drive northcote house
EX4 4QJ Exeter
Regno Unito

Regione

South West (England) Devon Devon CC

Tipo di attività

Higher or Secondary Education Establishments

Collegamenti

Contatta l’organizzazione Sito web

Partecipazione a programmi di R&I dell'UE

Rete di collaborazione HORIZON

Altri finanziamenti

€ 0,00